Ruiying Li
Dr. Ruiying Li is an assistant professor in the School of Reliability and Systems Engineering at Beihang University, where she also works in Science & Technology on Reliability & Environmental Engineering Laboratory. She obtained her B.E. and Ph.D. degrees in Reliability Engineering from Beihang University in 2004 and 2009, respectively. She was a visiting scholar in the Department of Systems and Industrial Engineering at the University of Arizona from 2013 to 2014. She is the author of more than forty scholarly papers covering various areas of network reliability and systems reliability. Her paper was awarded the Front-Runner 5000 in China. She is or was the PI of projects of the National Natural Science Foundation of China (NSFC) and Beijing Natural Science Foundation (BNSF). She is a member of IEEE.

Ruiying Li
Dr. Ruiying Li is an assistant professor in the School of Reliability and Systems Engineering at Beihang University, where she also works in Science & Technology on Reliability & Environmental Engineering Laboratory. She obtained her B.E. and Ph.D. degrees in Reliability Engineering from Beihang University in 2004 and 2009, respectively. She was a visiting scholar in the Department of Syst...read more

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Once the system reliability goal has been deﬁned, reliability allocation must be performed to the subsystems and components. However, the widely used reliability allocation methods are not capable of dealing with complex network structure. An improved Advisory Group on the Reliability of Electronic Equipment (AGREE) allocation method is proposed for networked system. In the proposed procedure, both the complexity and the importance of components are explicitly considered. Analytic algorithms and Monte Carlo simulation are applied to calculate the reliability of such a complex network structure. To determine the reliability allocation values, a heuristic algorithm is proposed, and an allocation adjustment procedure is provided to avoid inconsistent allocations caused by the same category of components involved in multiple missions. Several case studies show that the proposed method is practical and quite efﬁcient in handling such complex reliability allocation problems.

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